Through this assignment, I want to see the 5-mins walkshed and the bikeshed of the polling locations in South Boston. I will create 5-mins isochrones for each transportation mode, and I will calculate and compare them. I will also calculate the number of trees in each walkshed of the polling locations in South Boston and visualize it. I’m a strong believer that fresh air makes a significant impact on a person’s smart decision!
library(dplyr)
library(knitr)
library(osmdata)
library(opentripplanner)
library(tidyverse)
library(sf)
library(ggthemes)
library(ggspatial)
library(splitstackshape)
MA_state_plane <- "+proj=lcc +lat_1=41.71666666666667 +lat_2=42.68333333333333 +lat_0=41 +lon_0=-71.5 +x_0=200000 +y_0=750000 +ellps=GRS80 +units=m +no_defs"
poll_location <- st_read(
"http://bostonopendata-boston.opendata.arcgis.com/datasets/f7c6dc9eb6b14463a3dd87451beba13f_5.kml?outSR=%7B%22latestWkid%22%3A2249%2C%22wkid%22%3A102686%7D")
boston_nhood <- st_read("http://bostonopendata-boston.opendata.arcgis.com/datasets/3525b0ee6e6b427f9aab5d0a1d0a1a28_0.kml?outSR=%7B%22latestWkid%22%3A2249%2C%22wkid%22%3A102686%7D") %>%
filter(Name == "South Boston")
south_boston_polls <- poll_location[boston_nhood, ]
opq(bbox = 'South Boston MA USA') %>%
add_osm_feature(key = 'highway') %>%
osmdata_xml(file = 'OTP/graphs/default/south_boston_street.osm')
south_boston_street_features <- opq(bbox = 'South Boston MA USA') %>%
add_osm_feature(key = 'highway') %>%
osmdata_sf()
south_boston_streets <- south_boston_street_features$osm_lines %>%
st_transform(crs = MA_state_plane)
ggplot(south_boston_streets) +
geom_sf() +
theme_map()
path_data <- file.path(getwd(), "OTP")
path_otp <- paste(path_data, "otp.jar",sep = "/")
otp_build_graph(otp = path_otp, dir = path_data, memory = 1024)
otp_setup(otp = path_otp, dir = path_data, memory =1024)
## 2020-10-07 12:41:54 OTP is loading and may take a while to be useable
## Router http://localhost:8080/otp/routers/default exists
## 2020-10-07 12:42:55 OTP is ready to use Go to localhost:8080 in your browser to view the OTP
otpcon <- otp_connect()
## Router http://localhost:8080/otp/routers/default exists
south_boston_polls <- poll_location[boston_nhood, ] %>%
st_transform(src = MA_state_plane)
I’m going to create 5-mins isochrones for walking and bicycling.
iso_5min_walk <-
otp_isochrone(otpcon = otpcon, fromPlace = south_boston_polls,
mode = "WALK", cutoffSec = 300) %>%
st_transform(crs = MA_state_plane) %>%
mutate(mode = "walk")
## Warning in otp_isochrone(otpcon = otpcon, fromPlace = south_boston_polls, :
## Failed to get isochrone with error: {"type":"FeatureCollection","features":
## [{"type":"Feature","geometry":null,"properties":{"time":
## 300},"id":"fid-c895b39_174fe96b228_-7f9d"}]}Failed to get
## isochrone with error: {"type":"FeatureCollection","features":
## [{"type":"Feature","geometry":null,"properties":{"time":
## 300},"id":"fid-c895b39_174fe96b228_-7f96"}]}Failed to get
## isochrone with error: {"type":"FeatureCollection","features":
## [{"type":"Feature","geometry":null,"properties":{"time":300},"id":"fid-
## c895b39_174fe96b228_-7f94"}]}
iso_5min_bike <-
otp_isochrone(otpcon = otpcon, fromPlace = south_boston_polls,
mode = "BICYCLE", cutoffSec = 300) %>%
st_transform(crs = MA_state_plane) %>%
mutate(mode = "bike")
iso_all_modes <- rbind(iso_5min_bike, iso_5min_walk)
right_side <- st_bbox(iso_all_modes)$xmax
left_side <- st_bbox(iso_all_modes)$xmin
top_side <- st_bbox(iso_all_modes)$ymax
bottom_side <- st_bbox(iso_all_modes)$ymin
ggplot(iso_all_modes) +
annotation_map_tile(zoomin = 0, progress = "none") +
geom_sf(aes(fill = mode), alpha = 0.5) +
geom_sf(data = south_boston_polls) +
coord_sf(xlim = c(left_side, right_side),
ylim = c(bottom_side, top_side), expand = FALSE) +
scale_fill_viridis_d(name = "Area that is reachable within 5 minutes",
labels = c("By bike", "By foot")) +
theme_map() +
theme(legend.background = element_rect(fill = alpha("white", 0.5),
color = "gray")) +
labs(caption = "Basemap Copyright OpenStreetMap contributors")
## Loading required namespace: raster
right_side <- st_bbox(iso_all_modes)$xmax
left_side <- st_bbox(iso_all_modes)$xmin
top_side <- st_bbox(iso_all_modes)$ymax
bottom_side <- st_bbox(iso_all_modes)$ymin
ggplot(iso_all_modes) +
annotation_map_tile(zoomin = 0, type = "stamenbw", progress = "none") +
geom_sf(aes(fill = mode), alpha = 0.5) +
geom_sf(data = south_boston_polls) +
coord_sf(xlim = c(left_side, right_side),
ylim = c(bottom_side, top_side), expand = FALSE) +
scale_fill_viridis_d(name = "Area that is reachable within 5 minutes",
labels = c("By bike", "By foot")) +
theme_map() +
theme(legend.background = element_rect(fill = alpha("white", 0.5),
color = "gray")) +
labs(caption = "Basemap Copyright OpenStreetMap contributors")
south_boston_polls <- st_difference(south_boston_polls)
## although coordinates are longitude/latitude, st_difference assumes that they are planar
iso_areas <- iso_all_modes %>%
mutate(area = st_area(iso_all_modes)) %>%
st_set_geometry(NULL) %>%
pivot_wider(names_from = mode, values_from = area)
## Warning: Values are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list` to suppress this warning.
## * Use `values_fn = length` to identify where the duplicates arise
## * Use `values_fn = {summary_fun}` to summarise duplicates
iso_areas <- iso_areas %>%
filter(walk != "NULL") %>%
filter(str_detect(walk,"c")==FALSE) %>%
filter(str_detect(bike,"c")==FALSE)
## Warning in stri_detect_regex(string, pattern, negate = negate, opts_regex =
## opts(pattern)): argument is not an atomic vector; coercing
ggplot(iso_areas,
aes(x = as.numeric(walk), y = as.numeric(bike))) +
geom_point() +
scale_x_continuous(name =
"Area within a five-minute walking distance\nof a polling location(square km)",
breaks = breaks <- seq(10000, 130000, by = 20000),
labels = breaks / 1000000) +
scale_y_continuous(name =
"Area within a five-minute biking distance\nof a polling location\n(square km)",
breaks = breaks <- seq(0, 700000, by = 100000),
labels = breaks / 1000000) +
theme_bw()
tree <- st_read("http://bostonopendata-boston.opendata.arcgis.com/datasets/ce863d38db284efe83555caf8a832e2a_1.kml", quiet = TRUE)
south_boston_tree <- tree[boston_nhood, ]
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
ggplot(boston_nhood) +
geom_sf() +
geom_sf(data = south_boston_tree)
south_boston_tree <- south_boston_tree %>%
st_transform(MA_state_plane)
tree_poll_walk <- south_boston_tree [iso_5min_walk, ]
ggplot(tree_poll_walk) +
geom_sf(data = iso_5min_walk, fill = "royalblue3", alpha = 0.5, color = NA) +
geom_sf(data = tree_poll_walk , color = "darkgreen",size = 0.3) +
theme_map()
south_boston_tree <- south_boston_tree %>%
st_join(tree_poll_walk) %>%
mutate(by_tree = !is.na(Name.y))
n_tree_by_walk <- sum(south_boston_tree$by_tree)
n_tree_by_walk
## [1] 411
n_south_boston_tree <- length(south_boston_tree$by_tree)
pct_tree_by_walk <- n_tree_by_walk/n_south_boston_tree
pct_tree_by_walk
## [1] 0.06210335
left_side <- st_bbox(south_boston_tree)$xmin
top_side <- st_bbox(south_boston_tree)$ymax
ggplot(south_boston_tree) +
geom_sf(fill = "lightgray", color = "darkgray") +
geom_sf(data = south_boston_tree, size = 0.3,
aes(color = by_tree)) +
scale_color_manual(values = c("firebrick3", "darkgreen"),
name = "South Boston Trees \nby a distance to poll locations",
labels = c("No Tree within 5-min walk",
"Tree within 5-min walk")) +
annotation_scale(location = "br") +
annotation_north_arrow(location = "tr",
style = north_arrow_minimal()) +
annotate(geom = "text", x = right_side, y = bottom_side+200,
label = paste("Of the ",
prettyNum(n_south_boston_tree, big.mark = ","),
" trees in South Boston,\n",
prettyNum(n_tree_by_walk, big.mark = ","),
" (",
prettyNum(100*pct_tree_by_walk, digits = 0),
"%) are within \n5-mins walk from polling locations",
sep = ""), size = 3) +
theme_map() +
theme(panel.background = element_rect(fill = "aliceblue"),
legend.background = element_rect(fill = alpha("white", 0.5),
color = "gray"))